arXiv research

Method finds multiple noisy graph templates in large graphs.

Matched filters reveal optimal normalization methods for different market participants.

Improved real-time UAV terrain following with RVM-RLS filter.

EnFF uses flows to speed up DA in high dimensions.

A new filter reduces density fitting to a linear solve, improving performance on nonlinear systems.

Improved change point detection using matched filters for non-parametric tests.

Increasing availability of vehicle GPS data has created potentially transformative opportunities for traffic management, route planning and other location-based services. Critical to the utility of the data is their accuracy. Map-matching is the process of improving the accuracy by aligning GPS data with the road netwo…

Bayesian method learns optimal momentum for landmark matching.

New filters match advanced composition for adaptive privacy, with practical constants.

The paper audits trading filters, finding a high save-to-miss ratio.

This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in on…

The paper introduces a quantum state system to count perfect matchings in graphs.

Robust Kalman filtering method for outlier detection.

The problem of finding the vertex correspondence between two noisy graphs with different number of vertices where the smaller graph is still large has many applications in social networks, neuroscience, and computer vision. We propose a solution to this problem via a graph matching matched filter: centering and padding…

HKF uses neural networks to adapt Kalman filters for dynamic channel tracking.

Unified framework for self-supervised learning via latent distribution matching.

Paper develops efficient recursive learning for multi-channel systems with heterogeneous dynamics.

Deep learning improves GW signal detection efficiency and robustness.

Paper uses VAEs to detect radar targets in complex noise.

Filtering data with a pre-trained model improves multimodal contrastive learning performance.

This paper presents a fast Bayesian filtering technique for state estimation.

A new ensemble filter uses transport maps and MMD optimization for high-dimensional data assimilation.

New method combines ODE filters and numerical quadrature to propagate model uncertainty.

A new ML-based filter improves data assimilation for nonlinear systems.

A bandit problem with filtered Poisson process data.

EnCF improves data assimilation for implicit, non-smooth observations.

ATPF combines PF and EnKF for better inference in complex systems.

Model accurately gates ocean microbes from high-frequency flow cytometry data.

Recommender systems play a central role in providing individualized access to information and services. This paper focuses on collaborative filtering, an approach that exploits the shared structure among mind-liked users and similar items. In particular, we focus on a formal probabilistic framework known as Markov rand…

In general, recommendation can be viewed as a matching problem, i.e., match proper items for proper users. However, due to the huge semantic gap between users and items, it's almost impossible to directly match users and items in their initial representation spaces. To solve this problem, many methods have been studied…

Study explains Zipf's law using geometric mechanisms from a finite alphabet.

A new framework for efficient sequence maps using Bayesian filtering and covariance.

This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are generally considered unusable for speech synthesis. First, we predict fundamental frequency and voicing information from MFCCs with an autoreg…

Latent-EnSF improves data assimilation for high-dimensional systems with sparse observations.

A new method for signal recovery in high dimensions using projections and diffusion models.

Kernel Three-Pass Regression Filter improves forecasting efficiency for nonlinear dependencies.

In remote sensing, it is often challenging to acquire or collect a large dataset that is accurately labeled. This difficulty is usually due to several issues, including but not limited to the study site's spatial area and accessibility, errors in the global positioning system (GPS), and mixed pixels caused by an image'…

A new method for estimating uncertainties in neural ODEs without numerical integration.

SC-Net learns interpretable filters for inverse problems, achieving optimal convergence and super-resolution.

We study the sample complexity of learning one-hidden-layer convolutional neural networks (CNNs) with non-overlapping filters. We propose a novel algorithm called approximate gradient descent for training CNNs, and show that, with high probability, the proposed algorithm with random initialization grants a linear conve…

Compressed Monte Carlo improves efficiency in Bayesian inference.

XploVAE improves recommendation by balancing known and novel items.

This paper presents, evaluates, and discusses a new software tool to automatically build Dynamic Bayesian Networks (DBNs) from ordinary differential equations (ODEs) entered by the user. The DBNs generated from ODE models can handle both data uncertainty and model uncertainty in a principled manner. The application, na…

Social-based recommendation systems exploit the selections of friends to combat the data sparsity on user preferences, and improve the recommendation accuracy of the collaborative filtering strategy. The main challenge is to capture and weigh friends' preferences, as in practice they do necessarily match. In this paper…

Consider the task of estimating a 3-order $n \times n \times n$ tensor from noisy observations of randomly chosen entries in the sparse regime. We introduce a similarity based collaborative filtering algorithm for estimating a tensor from sparse observations and argue that it achieves sample complexity that nearly matc…

Method detects errors in numerical data using regression models.

Graph Neural Nets (GNNs) have received increasing attentions, partially due to their superior performance in many node and graph classification tasks. However, there is a lack of understanding on what they are learning and how sophisticated the learned graph functions are. In this work, we propose a dissection of GNNs …

New methods for skill rating in sports using state-space models.